



'''simple docstring'''


import os

import jsonlines
import numpy as np
from tqdm import tqdm


lowercase_   :					List[Any]        =     2048
lowercase_   :					List[Any]        =     4096
lowercase_   :					Optional[Any]        =     42
lowercase_   :					Optional[Any]        =     os.environ.pop('''PROCESS_TRAIN''', '''false''')
lowercase_   :					Dict        =     {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   str    ):
						def choose_first(lowercase_     :   Dict			,	lowercase_     :   Tuple=False    ):
												assert isinstance(UpperCamelCase_			,	UpperCamelCase_    )
												if len(UpperCamelCase_    ) == 1:
																		lowercase   =				answer[0]
																		return {k: [answer[k]] for k in answer} if is_long_answer else answer
												for a in answer:
																		if is_long_answer:
																								lowercase   =				{k: [a[k]] for k in a}
																		if len(a["""start_token"""]    ) > 0:
																								break
												return a

						lowercase   =				{"""id""": example["""id"""]}
						lowercase   =				example["""annotations"""]
						lowercase   =				annotation["""yes_no_answer"""]
						if 0 in yes_no_answer or 1 in yes_no_answer:
												lowercase   =				["""yes"""] if 1 in yes_no_answer else ["""no"""]
												lowercase   =				lowercase   =				[]
												lowercase   =				lowercase   =				[]
												lowercase   =				["""<cls>"""]
						else:
												lowercase   =				["""short"""]
												lowercase   =				choose_first(annotation["""short_answers"""]    )
												if len(out["""start_token"""]    ) == 0:
																		# answer will be long if short is not available
																		lowercase   =				["""long"""]
																		lowercase   =				choose_first(annotation["""long_answer"""]			,	is_long_answer=UpperCamelCase_    )
																		lowercase   =				[]
												answer.update(UpperCamelCase_    )

						# disregard some samples
						if len(answer["""start_token"""]    ) > 1 or answer["start_token"] == answer["end_token"]:
												lowercase   =				True
						else:
												lowercase   =				False

						lowercase   =				["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
						if not all(isinstance(answer[k]			,	UpperCamelCase_    ) for k in cols    ):
												raise ValueError("""Issue in ID"""			,	example["""id"""]    )

						return answer







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[Any]			,	lowercase_     :   str=False    ):
						lowercase   =				_get_single_answer(UpperCamelCase_    )
						# bytes are of no use
						del answer["start_byte"]
						del answer["end_byte"]

						# handle yes_no answers explicitly
						if answer["category"][0] in ["yes", "no"]:  # category is list with one element
												lowercase   =				example["""document"""]["""tokens"""]
												lowercase   =				[]
												for i in range(len(doc["""token"""]    )    ):
																		if not doc["is_html"][i]:
																								context.append(doc["""token"""][i]    )
												return {
												    "context": " ".join(UpperCamelCase_    ),
												    "answer": {
												        "start_token": -100,  # ignore index in cross-entropy
												        "end_token": -100,  # ignore index in cross-entropy
												        "category": answer["category"],
												        "span": answer["category"],  # extra
												    },
												}

						# later, help in removing all no answers
						if answer["start_token"] == [-1]:
												return {
												    "context": "None",
												    "answer": {
												        "start_token": -1,
												        "end_token": -1,
												        "category": "null",
												        "span": "None",  # extra
												    },
												}

						# handling normal samples

						lowercase   =				["""start_token""", """end_token"""]
						answer.update({k: answer[k][0] if len(answer[k]    ) > 0 else answer[k] for k in cols}    )  # e.g. [10] == 10

						lowercase   =				example["""document"""]["""tokens"""]
						lowercase   =				answer["""start_token"""]
						lowercase   =				answer["""end_token"""]

						lowercase   =				[]
						for i in range(len(doc["""token"""]    )    ):
												if not doc["is_html"][i]:
																		context.append(doc["""token"""][i]    )
												else:
																		if answer["start_token"] > i:
																								start_token -= 1
																		if answer["end_token"] > i:
																								end_token -= 1
						lowercase   =				""" """.join(context[start_token:end_token]    )

						# checking above code
						if assertion:
												lowercase   =				doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
												lowercase   =				doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
												lowercase   =				""" """.join([old[i] for i in range(len(UpperCamelCase_    )    ) if not is_html[i]]    )
												if new != old:
																		print("""ID:"""			,	example["""id"""]    )
																		print("""New:"""			,	UpperCamelCase_			,	end="""\n"""    )
																		print("""Old:"""			,	UpperCamelCase_			,	end="""\n\n"""    )

						return {
						    "context": " ".join(UpperCamelCase_    ),
						    "answer": {
						        "start_token": start_token,
						        "end_token": end_token - 1,  # this makes it inclusive
						        "category": answer["category"],  # either long or short
						        "span": new,  # extra
						    },
						}







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Union[str, Any]			,	lowercase_     :   List[str]			,	lowercase_     :   Any=2048			,	lowercase_     :   Optional[int]=4096			,	lowercase_     :   Union[str, Any]=True    ):
						lowercase   =				get_context_and_ans(UpperCamelCase_			,	assertion=UpperCamelCase_    )
						lowercase   =				out["""answer"""]

						# later, removing these samples
						if answer["start_token"] == -1:
												return {
												    "example_id": example["id"],
												    "input_ids": [[-1]],
												    "labels": {
												        "start_token": [-1],
												        "end_token": [-1],
												        "category": ["null"],
												    },
												}

						lowercase   =				tokenizer(example["""question"""]["""text"""]			,	out["""context"""]    ).input_ids
						lowercase   =				input_ids.index(tokenizer.sep_token_id    ) + 1

						# return yes/no
						if answer["category"][0] in ["yes", "no"]:  # category is list with one element
												lowercase   =				[]
												lowercase   =				[]
												lowercase   =				input_ids[:q_len]
												lowercase   =				range(UpperCamelCase_			,	len(UpperCamelCase_    )			,	max_length - doc_stride    )
												for i in doc_start_indices:
																		lowercase   =				i + max_length - q_len
																		lowercase   =				input_ids[i:end_index]
																		inputs.append(q_indices + slice    )
																		category.append(answer["""category"""][0]    )
																		if slice[-1] == tokenizer.sep_token_id:
																								break

												return {
												    "example_id": example["id"],
												    "input_ids": inputs,
												    "labels": {
												        "start_token": [-100] * len(UpperCamelCase_    ),
												        "end_token": [-100] * len(UpperCamelCase_    ),
												        "category": category,
												    },
												}

						lowercase   =				out["""context"""].split()
						lowercase   =				splitted_context[answer["""end_token"""]]
						lowercase   =				len(
						    tokenizer(
						        """ """.join(splitted_context[: answer["""start_token"""]]    )			,	add_special_tokens=UpperCamelCase_			,	).input_ids    )
						lowercase   =				len(
						    tokenizer(""" """.join(splitted_context[: answer["""end_token"""]]    )			,	add_special_tokens=UpperCamelCase_    ).input_ids    )

						answer["start_token"] += q_len
						answer["end_token"] += q_len

						# fixing end token
						lowercase   =				len(tokenizer(UpperCamelCase_			,	add_special_tokens=UpperCamelCase_    ).input_ids    )
						if num_sub_tokens > 1:
												answer["end_token"] += num_sub_tokens - 1

						lowercase   =				input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
						lowercase   =				answer["""start_token"""]
						lowercase   =				answer["""end_token"""]

						if assertion:
												lowercase   =				tokenizer.decode(UpperCamelCase_    )
												if answer["span"] != new:
																		print("""ISSUE IN TOKENIZATION"""    )
																		print("""OLD:"""			,	answer["""span"""]    )
																		print("""NEW:"""			,	UpperCamelCase_			,	end="""\n\n"""    )

						if len(UpperCamelCase_    ) <= max_length:
												return {
												    "example_id": example["id"],
												    "input_ids": [input_ids],
												    "labels": {
												        "start_token": [answer["start_token"]],
												        "end_token": [answer["end_token"]],
												        "category": answer["category"],
												    },
												}

						lowercase   =				input_ids[:q_len]
						lowercase   =				range(UpperCamelCase_			,	len(UpperCamelCase_    )			,	max_length - doc_stride    )

						lowercase   =				[]
						lowercase   =				[]
						lowercase   =				[]
						lowercase   =				[]  # null, yes, no, long, short
						for i in doc_start_indices:
												lowercase   =				i + max_length - q_len
												lowercase   =				input_ids[i:end_index]
												inputs.append(q_indices + slice    )
												assert len(inputs[-1]    ) <= max_length, "Issue in truncating length"

												if start_token >= i and end_token <= end_index - 1:
																		lowercase   =				start_token - i + q_len
																		lowercase   =				end_token - i + q_len
																		answers_category.append(answer["""category"""][0]    )  # ["short"] -> "short"
												else:
																		lowercase   =				-100
																		lowercase   =				-100
																		answers_category.append("""null"""    )
												lowercase   =				inputs[-1][start_token : end_token + 1]

												answers_start_token.append(UpperCamelCase_    )
												answers_end_token.append(UpperCamelCase_    )
												if assertion:
																		if new != old and new != [tokenizer.cls_token_id]:
																								print("""ISSUE in strided for ID:"""			,	example["""id"""]    )
																								print("""New:"""			,	tokenizer.decode(UpperCamelCase_    )    )
																								print("""Old:"""			,	tokenizer.decode(UpperCamelCase_    )			,	end="""\n\n"""    )
												if slice[-1] == tokenizer.sep_token_id:
																		break

						return {
						    "example_id": example["id"],
						    "input_ids": inputs,
						    "labels": {
						        "start_token": answers_start_token,
						        "end_token": answers_end_token,
						        "category": answers_category,
						    },
						}







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   int			,	lowercase_     :   Union[str, Any]			,	lowercase_     :   Any=2048			,	lowercase_     :   Union[str, Any]=4096			,	lowercase_     :   Optional[Any]=False    ):
						lowercase   =				get_strided_contexts_and_ans(
						    UpperCamelCase_			,	UpperCamelCase_			,	doc_stride=UpperCamelCase_			,	max_length=UpperCamelCase_			,	assertion=UpperCamelCase_			,	)

						return example







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Any			,	lowercase_     :   int    ):
						with jsonlines.open(UpperCamelCase_			,	"""a"""    ) as writer:
												for example in tqdm(UpperCamelCase_			,	total=len(UpperCamelCase_    )			,	desc="""Saving samples ... """    ):
																		lowercase   =				example["""labels"""]
																		for ids, start, end, cat in zip(
																		    example["""input_ids"""]			,	labels["""start_token"""]			,	labels["""end_token"""]			,	labels["""category"""]			,	):
																								if start == -1 and end == -1:
																														continue  # leave waste samples with no answer
																								if cat == "null" and np.random.rand() < 0.6:
																														continue  # removing 50 % samples
																								writer.write(
																								    {
																								        """input_ids""": ids,
																								        """start_token""": start,
																								        """end_token""": end,
																								        """category""": CATEGORY_MAPPING[cat],
																								    }    )


if __name__ == "__main__":
						from datasets import load_dataset

						from transformers import BigBirdTokenizer

						lowercase_   :					int        =     load_dataset('''natural_questions''')
						lowercase_   :					Union[str, Any]        =     BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

						lowercase_   :					str        =     data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

						lowercase_   :					Optional[int]        =     {
						    '''tokenizer''': tokenizer,
						    '''doc_stride''': DOC_STRIDE,
						    '''max_length''': MAX_LENGTH,
						    '''assertion''': False,
						}
						lowercase_   :					Union[str, Any]        =     data.map(prepare_inputs, fn_kwargs=fn_kwargs)
						lowercase_   :					Tuple        =     data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
						print(data)

						np.random.seed(SEED)
						lowercase_   :					Tuple        =     '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
						save_to_disk(data, file_name=cache_file_name)



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""poolformer"""







   def __init__(     self						:						str   ,   lowerCamelCase_						:						Any=3   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Optional[Any]=3   ,   lowerCamelCase_						:						List[str]=4.0   ,   lowerCamelCase_						:						Tuple=[2, 2, 6, 2]   ,   lowerCamelCase_						:						Optional[Any]=[64, 128, 320, 512]   ,   lowerCamelCase_						:						Tuple=[7, 3, 3, 3]   ,   lowerCamelCase_						:						Optional[Any]=[4, 2, 2, 2]   ,   lowerCamelCase_						:						Dict=[2, 1, 1, 1]   ,   lowerCamelCase_						:						Tuple=4   ,   lowerCamelCase_						:						Optional[int]=0.0   ,   lowerCamelCase_						:						Union[str, Any]="gelu"   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=1E-5   ,   lowerCamelCase_						:						int=0.0_2   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      stride
    UpperCamelCase			  =      padding
    UpperCamelCase			  =      pool_size
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      mlp_ratio
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      patch_sizes
    UpperCamelCase			  =      strides
    UpperCamelCase			  =      num_encoder_blocks
    UpperCamelCase			  =      drop_path_rate
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      use_layer_scale
    UpperCamelCase			  =      layer_scale_init_value
    UpperCamelCase			  =      initializer_range
    super().__init__(**lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				version.parse("""1.11"""		)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]				)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return 2E-3
